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¿Cuál matriz de pesos espaciales?. Un enfoque sobre selección de modelos
[Which spatial weighting matrix? An approach for model selection]

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  • Herrera Gómez, Marcos
  • Mur Lacambra, Jesús
  • Ruiz Marín, Manuel

Abstract

In spatial econometrics, it is customary to specify a weighting matrix, the so-called W matrix. The decision is important because the choice of W matrix determines the rest of the analysis. However, the procedure is not well defined and, usually, reflects the priors of the user. In the paper, we revise the literature looking for criteria to help with this problem. Also, a new nonparametric procedure is introduced. Our proposal is based on a measure of the information, conditional entropy, that uses information present in the data. We compare these alternatives by means of a Monte Carlo experiment.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 37585.

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Date of creation: 2011
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Handle: RePEc:pra:mprapa:37585

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Keywords: Econometría espacial; Selección de modelos; Entropía simbólica;

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  1. Luisa Corrado & Bernard Fingleton, 2011. "Where is the Economics in Spatial Econometrics?," SERC Discussion Papers 0071, Spatial Economics Research Centre, LSE.
  2. Raffaele Paci & Stefano Usai, 2009. "Knowledge flows across European regions," The Annals of Regional Science, Springer, vol. 43(3), pages 669-690, September.
  3. Olivier Parent & James P. Lesage, 2007. "Bayesian Model Averaging for Spatial Econometric Models ," University of Cincinnati, Economics Working Papers Series 2007-02, University of Cincinnati, Department of Economics.
  4. S Openshaw, 1977. "Optimal zoning systems for spatial interaction models," Environment and Planning A, Pion Ltd, London, vol. 9(2), pages 169-184, February.
  5. Conley, Timothy G. & Molinari, Francesca, 2005. "Spatial Correlation Robust Inference with Errors in Location or Distance," Working Papers 05-12, Cornell University, Center for Analytic Economics.
  6. Matilla-Garci­a, Mariano & Ruiz Mari­n, Manuel, 2008. "A non-parametric independence test using permutation entropy," Journal of Econometrics, Elsevier, vol. 144(1), pages 139-155, May.
  7. L W Hepple, 1995. "Bayesian techniques in spatial and network econometrics: 1. Model comparison and posterior odds," Environment and Planning A, Pion Ltd, London, vol. 27(3), pages 447-469, March.
  8. Peter Burridge, 2012. "Improving the J Test in the SARAR Model by Likelihood-based Estimation," Spatial Economic Analysis, Taylor & Francis Journals, vol. 7(1), pages 75-107, March.
  9. Hansen, Bruce E. & Racine, Jeffrey S., 2012. "Jackknife model averaging," Journal of Econometrics, Elsevier, vol. 167(1), pages 38-46.
  10. Henk Folmer & Johan Oud, 2008. "How to get rid of W: a latent variables approach to modelling spatially lagged variables," Environment and Planning A, Pion Ltd, London, vol. 40(10), pages 2526-2538, October.
  11. P Bodson & D Peeters, 1975. "Estimation of the coefficients of a linear regression in the presence of spatial autocorrelation. An application to a Belgian labour-demand function," Environment and Planning A, Pion Ltd, London, vol. 7(4), pages 455-472, April.
  12. Peter Burridge & Bernard Fingleton, 2010. "Bootstrap Inference in Spatial Econometrics: the J-test," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 93-119.
  13. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
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